University of Hertfordshire

By the same authors

Pigmented Skin Lesions Classification Using Data Driven Subsets of Image Features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publication10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149592
DOIs
Publication statusPublished - Jul 2019
Event10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece
Duration: 15 Jul 201917 Jul 2019

Publication series

Name10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Conference

Conference10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
CountryGreece
CityPatras
Period15/07/1917/07/19

Abstract

In this paper we present an architecture for identification of pigmented skin lesions from dermatoscopic images. The architecture used a large number of image features and was evaluated with several classification algorithms on different feature subsets as extracted from feature ranking. The best performing classification algorithm was the support vector machines using polynomial kernel function with classification accuracy equal to 74.69% and the most precisely classified skin lesion type between seven different skin pathologies was nevus with accuracy equal to 94.38%.

ID: 19521979